Next POI Recommender System: Multi-view Representation Learning for Outstanding Performance in Various Context

Yeonghwan Jeon, Junhyung Kim
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引用次数: 1

Abstract

Location-based Social Networks (LBSNs) are software service that enable a user to find knowledge and to socialize with other users by offering other user's contents (e.g. reviews, photos, etc.) to a user. This LBSNs have many sub-fields, but Point-of-Interest (POI) recommendation is the most important. Because it is related to the growth of Small and Medium Enterprise (SME) by increasing visitation rate. Generally, it should be possible to respond to various contexts of users in POI recommendation. These contexts are very various and complex, but we define mainly three contexts based on user behavior in local domain. However, each context is defined by different user behavior, so each model and performance are different on various evaluation criteria. In other words, no model is outstanding in all contexts. Therefore, this paper introduces how to define each context, how to make POI embedding for recommendation in empirical multi-view representation learning technique, and how to make optimized POI embedding which is outstanding performance in all contexts of POI recommendation, for various downstream tasks.
下一个POI推荐系统:多视图表示学习在各种环境下的卓越表现
基于位置的社交网络(LBSNs)是一种软件服务,使用户能够通过向用户提供其他用户的内容(例如评论,照片等)来查找知识并与其他用户进行社交。这个lbsn有许多子字段,但是兴趣点(POI)推荐是最重要的。因为它关系到中小企业的成长,通过提高访问量。一般来说,在POI推荐中应该能够响应用户的各种上下文。这些上下文是非常多样和复杂的,但我们主要根据用户在局部域的行为定义了三种上下文。然而,每个上下文都是由不同的用户行为定义的,因此每个模型和性能在不同的评估标准上是不同的。换句话说,没有一个模型在所有环境中都是杰出的。因此,本文介绍了如何定义每个上下文,如何在经验多视图表示学习技术中进行推荐的POI嵌入,以及如何针对各种下游任务进行优化的POI嵌入,这是POI推荐在所有上下文中的突出性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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